Background: Acute appendicitis is the most common cause for referral of patients with abdominal pains to emergency department of hospitals and appendectomy is the most common emergency operation. Despite of introduction of the various diagnostic methods unnecessary appendectomy rate is significant. Therefore, the use of artificial intelligence and machine learning methods as a tool to aid in the diagnosis can be timely and more accurate diagnosis, reduce length of stay in hospital and improve the treatment costs.Materials and Methods: During the developmental research, by studying literature and resources related to gastrointestinal diseases, variables affecting the diagnosis came together and were assessed by surgeons. During 2015, 181 cases of patients who underwent appendectomy was performed at the modarres Hospital constitute research database. Then, the support vector machine systems with different architectures implemented and compared to determine the best diagnostic function. Sensitivity, accuracy and specificity were used for evaluation.Results: The output obtained from the system of vector machine had sensitivity, specificity and accuracy of 91/7 percent, 96/2 percent and 95 percent which expresses its proper function in detecting acute appendicitis.Conclusion: According to the results, we can say that using designed support vector machine in diagnosis of acute appendicitiswill be effective in order to timely detect, prevent unnecessary appendectomy, reduction the patient's length of stay and health care costs.